A Modular Lower Limb Exoskeleton System with RL Based Walking Assistance Control

Lower limb exoskeleton for people suffering from unilateral weakness or even hemiparesis has attracted considerable interest in recent years. For walking assistance in such scenarios, the exoskeleton is expected to help generate symmetrically coordinated motion with the unaffected side. Challenges facing current exoskeletons are related to mechanical design and control strategy. In this paper, we approach the problem by presenting a modular lower limb exoskeleton system that consists of a modular hip orthosis and a modular knee orthosis, each has one electric actuator in the extension/flexion joints. Meanwhile, the exoskeleton system is an under-actuated system with some degree of freedoms remains passive, making it more complex than fully actuated robotic systems where classical PID controller can achieve satisfying performance. We than propose a reinforcement learning based controller to provide walking assistance based on a modeled leaderfollower system. And the walking assistance task can be reformulated as a motion trajectory tracking problem. The proposed control method is validated in simulation platform with data obtained from a healthy subject imitating hemiplegia patients with the designed set of single-side lower limb exoskeleton.